Delete app.py

app.py

@@ -1,87 +0,0 @@
-import gradio as gr
-import pandas as pd
-import joblib
-import matplotlib.pyplot as plt
-
-# Load the data and the trained model
-products = pd.read_csv("products_master.csv")
-model    = joblib.load("rf_model.joblib")
-
-# --- NEW: build short, readable labels for the dropdown ---
-def shorten(name, max_len=55):
-    # Use only the first comma-separated chunk (drops "8 HD Display, Wi-Fi, ..." stuff)
-    short = name.split(",")[0].strip()
-    # Keep color/edition if it appears at the end (e.g. "Tangerine", "Blue", "Pink")
-    parts = name.split(",")
-    if len(parts) > 1:
-        last = parts[-1].split("-")[0].strip()
-        # If the last chunk is short and looks like a color/variant, append it
-        if 0 < len(last) <= 25 and last.lower() != short.lower().split()[-1]:
-            short = f"{short} – {last}"
-    if len(short) > max_len:
-        short = short[:max_len-1] + "…"
-    return short
-
-# Build (label, value) pairs: label = short, value = full original name
-dropdown_choices = sorted(
-    [(shorten(n), n) for n in products["name"].tolist()],
-    key=lambda x: x[0]
-)
-# -----------------------------------------------------------
-
-def make_recommendation(product_name):
-    row = products[products["name"] == product_name].iloc[0]
-
-    # Build sentiment chart
-    fig, ax = plt.subplots(figsize=(5, 3))
-    counts = [row["pct_positive"], row["pct_neutral"], row["pct_negative"]]
-    colors = ["#2ecc71", "#95a5a6", "#e74c3c"]
-    ax.bar(["Positive", "Neutral", "Negative"], counts, color=colors, edgecolor="black")
-    ax.set_ylabel("% of reviews")
-    ax.set_title("Review sentiment breakdown")
-    ax.set_ylim(0, 1)
-
-    action = row["model_prediction"]
-    emoji  = {"Raise": "⬆️", "Hold": "➡️", "Drop": "⬇️"}[action]
-
-    summary = f"""
-### {shorten(row["name"], max_len=80)}
-
-**Brand:** {row["brand"]}  
-**Reviews analyzed:** {int(row["n_reviews"]):,}  
-**Average rating:** {row["avg_rating"]:.2f} / 5  
-**Average sentiment (VADER):** {row["avg_compound"]:.3f}
-
----
-
-### {emoji} Recommendation: **{action} the price**
-
-| Metric | Current | Recommended |
-|---|---|---|
-| Price | ${row["current_price"]:.2f} | ${row["recommended_price"]:.2f} |
-| Profit per unit | ${row["current_profit_per_unit"]:.2f} | ${row["recommended_profit_per_unit"]:.2f} |
-
-**Estimated monthly profit change:** **${row["monthly_profit_change"]:,.2f}**
-"""
-    return summary, fig
-
-
-with gr.Blocks(title="Amazon Electronics Pricing Recommender") as demo:
-    gr.Markdown("# 🛒 Amazon Electronics Pricing Recommender")
-    gr.Markdown("Pick a product to see its sentiment breakdown and a price recommendation based on a Random Forest model trained on real reviews.")
-
-    product_dropdown = gr.Dropdown(
-        choices=dropdown_choices,
-        label="Select a product",
-        value=dropdown_choices[0][1],
-    )
-    btn = gr.Button("Get Recommendation", variant="primary")
-
-    with gr.Row():
-        output_text  = gr.Markdown()
-        output_chart = gr.Plot()
-
-    btn.click(make_recommendation, inputs=product_dropdown, outputs=[output_text, output_chart])
-    demo.load (make_recommendation, inputs=product_dropdown, outputs=[output_text, output_chart])
-
-demo.launch()